This project explores the factors influencing football players' performance by analyzing statistical and match-related data. It aims to build a comprehensive dataset and conduct detailed analyses to uncover insights into what drives players to excel in matches.
- Data Integration: Combine datasets related to player statistics, match information, and external factors to create a rich, unified dataset.
- Performance Prediction: Identify key variables and patterns to predict player performance, by prediticting the percetage of scoring.
- Data Sources: The dataset used in this project includes detailed player statistics, match information, and external factors. Key features are combined with match-related variables. All data is stored in the
datafolder, organized for easy access and analysis. - Code: The analysis and prediction process is executed in a Jupyter Notebook file
code.ipynb. The code covers data preprocessing, feature selection, and model building. The machine learning model is built using Random Forest Regressor, a robust algorithm that is ideal for regression tasks like goal prediction. The notebook also includes data visualizations to provide insights into the relationships between different features and player performance.
- Data Preprocessing: Cleaning and transforming raw data into a format suitable for model training.
- Visualization: Visualizing trends and distributions to gain a better understanding of player performance and key metrics.
- Model Training: Using the Random Forest algorithm to predict the number of goals scored by players.
- Evaluation: Assessing model performance using metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared score.
The insights from this analysis can help coaches, analysts, and fans understand the dynamics of player performance in international football, providing a data-driven perspective on the sport.
- player_id: An integer that uniquely identifies each player in the dataset.
- player_name: The full name of the player.
- player_age: The player’s age, recorded as an integer.
- nationality: The country or origin of the player.
- date_of_birth: The date on which the player was born.
- height_in_cm: The player’s height, measured in centimeters.
- position: The role or position the player holds on the field, e.g., forward, midfielder, defender, or goalkeeper.
- club_id: A unique identifier for the player’s club.
- game_id: A unique identifier for the match the player participated in.
- competition_id: A unique identifier for the competition or tournament in which the match occurred.
- season: The football season during which the match was played, represented in a YYYY format.
- match_date: The date the match took place.
- home_club_id: A unique identifier for the home team of the match.
- away_club_id: A unique identifier for the away team of the match.
- home_club_goals: The total number of goals scored by the home team in the match.
- away_club_goals: The total number of goals scored by the away team in the match.
- match_outcome: The outcome of the match for the player’s team, categorized as Win, Loss, or Draw.
- home_club_name: The full name of the home team.
- away_club_name: The full name of the away team.
- yellow_card: The number of yellow cards the player received during the match.
- red_cards: The number of red cards the player received during the match.
- goals: The total number of goals scored by the player during the match.
- assists: The total number of assists made by the player during the match.
- minutes_played: The total number of minutes the player was on the field during the match.
- club_name: The name of the club the player represents.
- competition_name: The name of the competition or league where the match took place.